Intelligent Sensor System for Early Illness Alerts in Senior Housing

用于老年住宅早期疾病警报的智能传感器系统

• 批准号: 8478491
• 负责人: MARILYN J RANTZ
• 金额: $ 62.99万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-16 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8478491
• 关键词: Activities of Daily Living; Acute; Affect; Age; Agitation; Algorithms; American; Beds; Blood Glucose; Breathing; Caregivers; Chronic; Chronic Disease; Clinical effectiveness; Complex; Data; Detection; Devices; Disease Management; Early Diagnosis; Early Intervention; Early treatment; Elderly; Environment; Equipment; Gait; Hand; Health; Health Care Costs; Health Personnel; Health Status; Healthcare; Home Nursing Care; Home environment; Hospitalization; Housing; Intervention; Intervention Studies; Left; Length of Stay; Life; Measures; Methods; Monitor; Morbidity - disease rate; Motion; Nursing Homes; Participant; Patient Self-Report; Pattern; Physiologic pulse; Pilot Projects; Population; Prevention; Prospective Studies; Pulse Oximetry; Recovery; Recruitment Activity; Sample Size; Sampling; Sleep; Speed; System; Time; United States; Weight; Work; active control; base; cost; cost effectiveness; functional decline; functional improvement; grasp; improved; information display; interest; mortality; multiple chronic conditions; prospective; public health relevance; sensor; telehealth; web based interface

DESCRIPTION: Chronic disease management is the biggest health care problem facing the United States today. In 2005, nearly 1 in 2 Americans (133 million) had at least one chronic condition, and 21% of the population had multiple chronic conditions. These numbers will steadily increase over the next 30 years. Chronic diseases especially affect older adults in whom exacerbations result in dramatic changes and decline in health status, hospitalization, complex treatment interventions, and high cost. Early illness recognition and early treatment is not only a key to improving health status with rapid recovery after an exacerbation of a chronic illness or acute illness, but also a key to reducing morbidity and mortality in older adults and controlling costs of health care. We propose to build on our recently completed pilot intervention study (NINR R21, Rantz, PI). In that study, we developed alerts for our intelligent sensor system (not telehealth that measures traditional vital signs, weight, pulse oximetry, blood sugar) and used it prospectively to measure functional ability in older adults and actually detected changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Inexpensive sensors are embedded in the environment, so subjects do not "have to use" or "wear" any devices. The R21 was to: 1) develop alerts based on the sensor data to notify health care providers of early signs of illness or functional decline so they could further evaluate and intervene with early treatment; 2) further develop and refine a web-based interface to display the sensor data to health care providers; and 3) determine the sample size for an intervention study that would measure the clinical effectiveness and cost-effectiveness of using the sensor system with alerts in elder housing. Now, we propose to conduct a prospective intervention study to measure the clinical effectiveness and cost effectiveness of using sensor data to detect early signs of illness or functional decline in older adults compared to usual health assessment. A larger sample of older adults will be recruited; they live in a different independent housing than where the pilot study was conducted. While preparing the staff in the different housing setting to work with alerts from our intelligent sensor system, we will adjust, if necessary, the algorithms for automated alerts or the web-based interface for health care providers. Following the prospective study, we will develop and refine ways of providing sensor information to older adults and informal caregivers to help them directly better manage changes in health status. Our intelligent sensor system enables early detection of illness or functional decline, the key to successful less invasive, time consuming, and expensive interventions. Helping older adults remain healthier, active, and control their chronic illnesses with early detection of changes in health status and early intervention by health care providers, can result in millions of people remaining independent as they age, avoiding or reducing debilitating and costly hospital stays, and for many, avoiding or delaying nursing home care. This application will be of interest to both NINR and NIA.

描述：慢性病管理是当今美国面临的最大医疗保健问题。 2005年，近20个美国人（1.33亿）至少有1个慢性病，而21％的人口患有多种慢性病。这些数字将在未来30年内稳步增加。慢性疾病尤其影响老年人，其中加剧导致健康状况，住院，复杂的治疗干预措施和高成本的急剧变化和下降。早期疾病识别和早期治疗不仅是加重慢性病或急性疾病后快速康复的健康状况的关键，而且还是降低老年人发病率和死亡率以及控制医疗保健成本的关键。 我们建议以我们最近完成的试点干预研究（NINR R21，Rantz，PI）为基础。在这项研究中，我们为智能传感器系统开发了警报（不是测量传统的生命体征，体重，脉搏血氧症，血糖，并前瞻性地使用它来测量老年人的功能能力，并实际检测到慢性疾病或急性疾病的变化，平均10天到2周，在通常的评估方法或自我报告的情况下。便宜的传感器嵌入了环境中，因此受试者不必“使用”或“佩戴”任何设备。 R21是：1）根据传感器数据开发警报，以通知医疗保健提供者的早期疾病迹象或功能下降，以便他们可以进一步评估和干预早期治疗； 2）进一步 开发和完善基于网络的界面，以向医疗保健提供者显示传感器数据； 3）确定一项干预研究的样本量，该研究将衡量使用传感器系统和老年住房中的警报的临床有效性和成本效益。 现在，我们建议进行一项前瞻性干预研究，以衡量使用传感器数据与通常的健康评估相比，使用传感器数据来检测老年人疾病或功能下降的早期迹象的临床有效性和成本效益。将招募更大的老年人样本；他们生活在进行试点研究的不同独立住房中。在为不同住房设置的工作人员准备与我们智能传感器系统的警报一起工作时，我们将在必要时调整自动警报的算法或 基于Web的医疗保健提供者界面。经过前瞻性研究，我们将开发并完善向老年人和非正式护理人员提供传感器信息的方法，以帮助他们直接更好地管理健康状况的变化。我们的智能传感器系统可以尽早发现疾病或功能下降，这是成功侵入性的关键，时间 消费和昂贵的干预措施。通过早期发现健康状况的变化和医疗保健提供者的早期干预，帮助老年人保持更健康，积极和控制其慢性疾病，可能会导致数百万的人随着年龄的增长而保持独立，避免或减少衰弱和昂贵的医院，并且对于许多人来说，避免或避免养育家庭护理。 NINR和NIA都将引起此应用程序。